diff --git a/complexity.py b/complexity.py
index 69d5ba1..ff7a2cd 100644
--- a/complexity.py
+++ b/complexity.py
@@ -22,8 +22,15 @@
 
 # Data pre-processing and import
 # from modules import mnist
-from modules import mnist 
+from modules import mnist
+from numpy.random import rand, multivariate_normal
+from numpy import arange, zeros, ones
+from scipy import dot
+import matplotlib.pyplot as plt
 
+from ite.cost.x_factory import co_factory
+from ite.cost.x_analytical_values import analytical_value_d_mmd
+from ite.cost.x_kernel import Kernel
 ####################################################
 
 '''
@@ -171,6 +178,83 @@ def bsda_active(datasets=[], baseline_clf=SVC(), N=100):
     print(baseline_clf.predict(BSDA_X_Test))
     print("Classification accuracy: ", round(baseline_clf.score(BSDA_X_Test, BSDA_y_Test), 3) * 100)
 
+def MMD():
+    # !/usr/bin/env python3
+
+    """ Demo for maximum mean discrepancy (MMD) estimators.
+
+    Analytical vs estimated value is illustrated for normal random variables.
+
+    """
+
+
+
+    def main():
+        # parameters:
+        dim = 1  # dimension of the distribution
+        num_of_samples_v = arange(100, 3 * 1000 + 1, 100)
+        cost_name = 'BDMMD_UStat'  # dim >= 1
+        # cost_name = 'BDMMD_VStat'  # dim >= 1
+        # cost_name = 'BDMMD_UStat_IChol'  # dim >= 1
+        # cost_name = 'BDMMD_VStat_IChol'  # dim >= 1
+
+        # initialization:
+        distr = 'normal'  # fixed
+        num_of_samples_max = num_of_samples_v[-1]
+        length = len(num_of_samples_v)
+        d_hat_v = zeros(length)  # vector of estimated divergence values
+
+        # RBF kernel (sigma = std / bandwith parameter):
+        kernel = Kernel({'name': 'RBF', 'sigma': 1})
+        # polynomial kernel (quadratic / cubic; c = offset parameter = 1):
+        # kernel = Kernel({'name': 'polynomial', 'exponent': 2, 'c': 1})
+        # kernel = Kernel({'name': 'polynomial', 'exponent': 3, 'c': 1})
+
+        co = co_factory(cost_name, mult=True, kernel=kernel)  # cost object
+
+        # distr, dim -> samples (y1<<y2), distribution parameters (par1,par2),
+        # analytical value (d):
+        if distr == 'normal':
+            # mean (m1,m2):
+            m1 = rand(dim)
+            m2 = rand(dim)
+
+            # (random) linear transformation applied to the data (l1,l2) ->
+            # covariance matrix (c1,c2):
+            l2 = rand(dim, dim)
+            l1 = rand(dim, dim)
+            c1 = dot(l1, l1.T)
+            c2 = dot(l2, l2.T)
+
+            # generate samples (y1~N(m1,c1), y2~N(m2,c2)):
+            y1 = multivariate_normal(m1, c1, num_of_samples_max)
+            y2 = multivariate_normal(m2, c2, num_of_samples_max)
+
+            par1 = {"mean": m1, "cov": c1}
+            par2 = {"mean": m2, "cov": c2}
+
+        else:
+            raise Exception('Distribution=?')
+
+        d = analytical_value_d_mmd(distr, distr, kernel, par1, par2)
+
+        # estimation:
+        for (tk, num_of_samples) in enumerate(num_of_samples_v):
+            d_hat_v[tk] = co.estimation(y1[0:num_of_samples],
+                                        y2[0:num_of_samples])  # broadcast
+            print("tk={0}/{1}".format(tk + 1, length))
+
+        # plot:
+        plt.plot(num_of_samples_v, d_hat_v, num_of_samples_v, ones(length) * d)
+        plt.xlabel('Number of samples')
+        plt.ylabel('MMD')
+        plt.legend(('estimation', 'analytical value'), loc='best')
+        plt.title("Estimator: " + cost_name)
+        plt.show()
+
+    if __name__ == "__main__":
+        main()
+
 
 def main():
     #baseline_clfs = [SVC(), GaussianNB(), DecisionTreeClassifier(), MLPClassifier(hidden_layer_sizes=(10,10,10,10,10,10), solver='lbfgs', alpha=2, random_state=1, activation='relu')]
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